23rd EGOS Colloquium in Vienna, 2007
Standing Work Group 2: Organizational Network Research
Learning in Project-Based Organizations: How Project Members’ Social Capital
Affects Effective Knowledge Transfer and Organizational Performance
(REG.NO. 38934)
Indre Maurer, Vera Bartsch, Mark Ebers, Svenja Knöpfler
University of Cologne
Chair for Business Administration, Corporate Development and Organization
(Prof. Dr. Mark Ebers)
Albertus-Magnus-Platz
D-50931 Köln
Tel.: ++49/(0)221-470-2857
Fax: ++49/(0)221-470-7883
e-Mail: MAURER@WISO.UNI-KOELN.DE
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Learning in Project-Based Organizations: How Project Members’ Social Capital
Affects Effective Knowledge Transfer and Organizational Performance
ABSTRACT
It is a major challenge facing project-based organizations to realize learning benefits across individual
projects. This study examines how different characteristics of project members’ intra-organizational
social capital facilitate knowledge transfer in project-based organizations, and how knowledge transfer, in turn, impacts organizational performance as measured by improved market access, product development, and project and process management. Based on an analysis of 218 projects in the engineering industry, the study extends earlier research by highlighting the discriminating role of knowledge
content for understanding how social capital and effective knowledge transfer drive organizational
performance in project-based organizations. We discuss implications of these findings for the literatures on project-based organization, knowledge management, and social capital.
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INTRODUCTION
Organizations increasingly rely on projects to carry out their business activities (Whittington, Pettigrew, Peck et al. 1999; Midler 1995). If most or all business activities of an organization are undertaken in the form of projects these organizations are referred to project-based organizations (Hobday
2000). Project-based organizations are the prevalent mode of organization in a variety of industries,
including among others advertising (Grabher 2002), construction (Winch 1989; Eccles 1981), film
(Bechky 2006), and fashion (Uzzi 1996).
Whereas the early literature mainly studied project organization and project management, more recent
research has focused on organizational learning as a key performance driver in project-based organizations (Söderlund 2004; Blindenbach-Driessen and van den Ende 2006). Intra-organizational learning
involves the creation of knowledge, its transfer between organizational units and its subsequent use
that results in an extension of the knowledge base of the receiving organizational unit (Argote and
Ophir 2002). Organizational learning, in general, and the ability effectively to transfer knowledge
from one organizational unit to another in particular are regarded as important preconditions for organizational performance (Hansen 2002; Argote et al. 2000; Almeida and Kogut 1999;). However, while
projects are widely recognized as being wellsprings of learning and innovation (Davies and Hobday
2005), project-level learning and innovation cannot easily be transferred to the level of the projectbased organization as a whole (DeFillippi and Arthur 1998; Lundin and Söderholm 1995). A number
of studies have identified ways of facilitating cross-project knowledge transfer, including the implementation of knowledge management systems (Koskinen 2004; Williams 2004; Cooper et al. 2002)
and IT-infrastructure (Laurikkala et al. 2002), project organization and management (Davies and
Brady 2000), the implementation of communities of practice (Brown and Duguid 2001; Wenger 2000)
and centralized “project baronies” that effect knowledge transfer by means of managerial interventions
(Gann and Salter 2003).
This study complements and extends this earlier research by analyzing how different characteristics of
project members’ intra-organizational social capital with their colleagues outside the project facilitate
the effective transfer of knowledge generated in inter-organizational projects within their home organizations and by examining at the same time to what extent the knowledge transferred leads to improvements in different dimensions of organizational performance. Although social capital has been
defined in a number of ways, the core intuition behind the notion is that it signifies an asset available
to actors that draws on these actors’ structural positions in a social network and the content of these
actors’ social relations (Gabbay and Leenders 1999; Portes 1998). It has been shown that organization
members’ social capital with external partners can facilitate the exchange, combination and application of knowledge and novel ideas (Kang et al. 2007; Inkpen and Tsang 2005; Uzzi and Lancaster
2003; Liebeskind et al. 1996). The present study complements an emerging line of research that has
studied how organization members’ intra-organizational social capital with their colleagues outside the
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project affects these learning processes (Reagans and McEvily 2003; Tsai 2001; Hansen 1999). Due to
their high barriers to knowledge transfer, project-based organizations represent a good context for
testing whether project members’ intra-organizational social capital can help to bridge barriers of
cross-project knowledge transfer and contributes to organizational learning and performance.
Our understanding of how social capital may facilitate knowledge transfer and thus affect organizational performance in project-based organizations is still incomplete (Bresnen et al. 2003). Most studies have illuminated but parts of the process chain linking features of organization members’ social
capital with knowledge transfer and subsequent organizational performance.
The great majority of studies have discussed the links between social capital, knowledge transfer and
organizational performance by implicitly inferring either knowledge transfer or subsequent performance effects (for exceptions see Yli-Renko et al. 2001; Tsai and Ghoshal 1998). Some research has
examined the impact of social capital on knowledge transfer (Perry-Smith 2006; Hansen et al. 2005;
Reagans and McEviley 2003; Tsai 2002). While this research in part rests on the presumption that
knowledge transfer has positive effects, it does not explicitly address possible performance implications. It thus remains in the dark whether or not the knowledge transferred is actually exploited and
does affect organizational performance. Other studies have focused on the association between social
capital and organizational performance (Balkundi and Harrison 2006; Rowley et al. 2000). While
these studies provide valuable insights, they are largely silent on the processes linking social capital
with performance outcomes.
Results from a cross-sectional study of 218 projects in the engineering industry confirm our hypotheses that two dimensions of project members’ intra-organizational social capital discriminately affect
the transfer of different kinds of knowledge contents and their respective performance outcomes. Specifically, findings show that structural features of project members’ social capital (i.e. the number of
intra-organizational ties) influence the transfer and application of project and process management
knowledge, while relational features of project members’ social capital (i.e. tie strength and trust) ease
the transfer of market and product knowledge. Our findings further indicate that the two kinds of
knowledge transferred, in turn, positively and discriminatingly mediate the relation between the structural dimension of social capital and project and process performance, on the one hand, and between
the relational dimension of social capital and product and market-related firm performance indicators
on the other.
This research makes three main theoretical contributions. First, the study adds to the literature on project-based organizations (Söderlund 2004) by identifying project members’ intra-organizational social
capital as an important driver for effective knowledge transfer and subsequent performance improvement in project-based organizations. Second, our research also contributes to the theory of social capital (Adler and Kwon 2002) by studying how intra-organizational social capital impacts learning and
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organizational performance. Specifically, the study highlights the mediating role of knowledge transfer processes in linking intra-organizational social capital and organizational performance; and it further demonstrates that the structural and relational dimensions of intra-organizational social capital
alleviate the transfer of different types of knowledge content, which in turn have implications for different dimensions of organizational performance. Third, this study also contributes to the literature on
knowledge management in organizations (Argote et al. 2003). In terms of knowledge attributes, this
literature has mainly focused on discriminating associations of codifiable and tacit knowledge with
various antecedents and consequences. The present study highlights how different dimensions of
knowledge content, namely whether knowledge concerns the external market and the firm’s products
or the internal processes of project and process management, discriminate across antecedents and outcomes of knowledge transfer.
THEORY AND HYPOTHESES
Knowledge Transfer and Performance in Project-Based Organizations
Organizational learning refers to the processes through which organizational units change as a result of
experience (Argote 1999). It comprises the creation of knowledge, its transfer, and use. It manifests
itself as changes of both the knowledge base of the receiving organizational unit and its performance
(Argote and Ophir 2002). It has been shown for project-based and other organizations that external
partners provide an important source of knowledge creation (Söderlund 2004; Dyer and Nobeoka
2000). Knowledge transfer is the sub-process through which one organizational unit shares her
knowledge with another actor (Argote and Ingram 2000). Knowledge transfer leads to the distribution
of knowledge within the firm across project boundaries, geographical locations, and time periods. The
subsequent exploitation of this knowledge refers to the application and use of knowledge by actors
who take action and produce performance outcomes (Choo and Bontis 2002). In project-based organizations and beyond, knowledge transfer has been acknowledged as a key driver affecting various aspects of organizational performance (Bresnen et al. 2003), for instance product innovation and profitability (Tsai 2001), new product development (Hansen 1999), and group efficiency and innovativeness
(Wong 2004).
Several authors have noted the difficulty of transferring knowledge in project-based organizations
(Gann and Salter 2000; DeFillippi and Arthur 1998; Lundin and Söderholm 1995). The one-off nature
of many projects and their uniqueness create discontinuities in terms of tasks, clients, and workforce
composition that inhibit knowledge transfer (DeFillippi and Arthur 1998; Prencipe and Tell 2001). To
the extent that project teams are locally dispersed with only loose contact with other members of the
firm, they may lack the opportunity to exchange experiences and new ideas with members from other
projects (Grabher 2002). Project members may also have little motivation to do so, as the just noted
circumstances also imply high cost of knowledge search and transfer. In addition, cross-project search
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and transfer costs can be amplified by competition between projects for scarce resources (Hansen et al.
2005). The benefits of knowledge exchange may also be unclear, as the transient nature of projects and
the situated nature of much of the generated knowledge cast into doubt whether learning experiences
and innovations will be applicable and effective beyond the context in which they were generated
(Bresnen et al. 2003). If at all, local, within-project search and knowledge exchange thus seems more
attractive (Stuart and Podolny 2007). In addition, project-based organizations often lack incentives and
formal structures for cross-project learning (Ekstedt et al. 1999). Finally, due to the situated nature of
much of the generated knowledge, project members may also lack the ability, and their organizational
unit the absorptive capacity, to recognize and understand the relevance of knowledge generated in
other projects (Tsai 2001), and for the reasons given above may then also lack the opportunity and
motivation to change this. In sum, this literature argues that lack of opportunity, motivation and ability, three core mechanisms of knowledge management (Argote et al. 2003), can impede effective
knowledge transfer in project-based organizations. As a result, the knowledge created inside the project’s boundaries is neither preserved nor available for the organization as a whole.
Social capital has been shown to affect actors’ opportunities, motivation, and abilities to share resources, knowledge and ideas (Adler and Kwon 2002). Thus, there is reason to expect that social capital might contribute to bridging the above noted barriers of knowledge transfer and in this way impact
organizational performance.
The Impact of Social Capital on Knowledge Transfer and Performance
The notion of social capital has begun to be discussed extensively in the scientific literature since the
mid-1980s, when Bourdieu (1985), Coleman (1988, 1990) and Burt (1992) offered their seminal contributions to the explication of the concept. Several reviews have documented the merits of research on
social capital (Adler and Kwon 2002; Gabbay and Leenders 2001; Baker 2000; Nahapiet and Ghoshal
1998).
Two main dimensions of social capital have been discussed in the literature. The structural dimension
of social capital refers to the pattern of ties that link actors. It focuses on the structural features of actors' relationships with other actors. These linkages present learning opportunities, as they enable social interaction and provide access to different knowledge bases (Baum et al. 2000; Powell et al.
1996). Actors occupying brokerage positions can also reap control benefits by exploiting their privileged access to information (Burt 2005). The relational dimension of social capital concerns the nature
of ties that characterize a specific relationship and govern transaction partners’ behavior within this
relationship; for instance, relations can be close or weak, and based on trust or norms as opposed to
contractual agreements. Depending on the nature of relations, their social capital may enhance actors’
motivation and ability to share, transfer and absorb knowledge through better cooperation, communication and a common understanding between exchange partners (Kale et al. 2000).
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Only few researchers in one way or another examined the links between intra-organizational social
capital, knowledge transfer and changes in organizational performance, yet so far not in the highly
challenging context of project-based organizations. Smith et al. (2005) find that the rate of new product and service introduction is a function of organization members’ ability to exchange and combine
knowledge, which in turn depends on whether top management team members and knowledge workers are linked by many direct and strong ties. Similarly, Tsai and Ghoshal (1998) show how social
interaction, trust and a shared vision between organizational subunits affect the transfer of information
concerning market trends, sources of suppliers, or ideas for product development, as well as the exchange of other resources. Further, their results disclose that resource exchange leads to value creation
for the firm in the form of product innovations. We extend this earlier research by examining how
different contents of the knowledge transferred discriminatingly mediate the relations between different dimensions of social capital and organizational performance.
The Impact of Knowledge Attributes on Knowledge Transfer
Research in knowledge management has shown that knowledge attributes affect how easily knowledge
can be exchanged within and across firm boundaries. The majority of pertinent research has scrutinized the discriminating characteristics of codified/un-codified and tacit/explicit knowledge. It has
been demonstrated that un-codified knowledge is more difficult to transfer than codified knowledge
(Zander and Kogut 1995) and that it is more challenging to transfer tacit knowledge than explicit
knowledge (Hansen 1999). Few other knowledge attributes have been studied. These include, for instance, whether knowledge originates within or outside the organization (Kane et al. 2005), is widely
shared among organization members or held by few (Thomas-Hunt et al. 2003), or is private or public
knowledge (Uzzi and Lancaster 2003).
We suggest that in addition to the just mentioned knowledge attributes, the ease of knowledge transfer
can also depend on the particular contents of the knowledge that is transferred. It has been argued that
for project-based organizations in particular, it is important to recognize and exploit two different
forms of knowledge: project-based technological capabilities as well as intra-organizational processes
and activities (Gann and Salter 2000). Therefore, we distinguish two kinds of knowledge contents.
Product/market knowledge, as we call it, concerns external market- and technology-related factors. It
for instance refers to knowledge about marketable products, product innovation and technological
advancements as well as knowledge of future market trends and opportunities both on the suppliers’
and the buyers’ side (Eriksson et al. 1997). Project/process management knowledge, as we call it, is
about intra-organizational processes and activities. It includes, for example, knowledge concerning
project management but also knowledge about internal organizational processes such as planning,
production, or logistics (Ingram 2005).
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In open interviews that we conducted, the industry experts and some of our respondents frequently
mentioned that these two knowledge contents differ in various respects. Our interview partners perceived product/market knowledge as being more complex and important because it represented the
core of the engineering projects, what they were about. Product/market knowledge was an arena for
innovation, where engineers generate new solutions and improve the performance of their organizations. In contrast, project/process management knowledge was perceived as a largely known and welltried tool kit for managerial support tasks. Our respondents felt that this knowledge arena provided
comparatively fewer learning opportunities, as thought they largely knew and had mastered the techniques and procedures of project and process management that were available.
In more theoretical terms, these two kinds of knowledge contents typically tend to differ in several
dimensions, at least in the context of the present study, the project-based engineering industry. Product/market knowledge mainly entails technological know-how and information concerning technical
problem solving (Eriksson et al. 1997). This kind of knowledge content is commonly described as
complex (Kotabe et al. 2003). Therefore, the quality of product market knowledge is difficult to assess
and to verify beforehand. Additionally, it is costly, difficult and slow to transfer across organizational
units (von Hippel 1994). Furthermore, product/market knowledge can be an important basis for competitive advantage, as it directly affects a firm’s financial performance through technological distinctiveness or the discovery and exploitation of novel markets. Those who hold or develop such valuable
knowledge may thus improve their internal status and career opportunities (Sutton and Hargadon
1996). As a consequence, product/market knowledge becomes a highly sensitive private asset that
cannot be easily accessed in a written form but must be voluntarily transferred in an exchange (Uzzi
1999).
Project and process management knowledge contains experiences from prior projects, for example
with respect to beneficial contract design, claims management, country specific safety requirements,
the proper definition of work packages, project interfaces, or the management of project milestones.
This kind of knowledge content frequently can be encoded in explicit terms in project handbooks or
post project documentations (Kim 2001). Project/process management knowledge is less “sticky” (von
Hippel 1994: 430) than more complex product/market knowledge and can thus be more easily identified, transferred with less effort and at lower cost (Zander and Kogut 1995). However, the effective
transfer of project/process management knowledge often requires extensive coordination throughout
the organization. This is because project and process management are commonly manifested in standard operating procedures that affect larger parts of the organization. By applying standard project and
process management procedures across organizational units, organizations can reap coordination benefits and realize synergies. The decision and implementation processes for project and process management procedures will thus involve some degree of coordination across organizational units.
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Obviously, there will be some variation within the two content categories with regard to the above
mentioned characteristics. However, according to our interviews and the literature cited above, in sum
the two kinds of knowledge contents will typically differ in the context studied here in terms of the
following characteristics: Product/market knowledge tends to be relatively more complex, private and
difficult to assess, whereas project/process management knowledge can be more easily encoded and
traded, yet will require more intense cross-unit coordination.
Based on the noted differences, we propose in the following that the knowledge contents transferred
across projects and organizational units influence how organization members’ social capital affects
organizational performance outcomes.
The Structural Dimension of Social Capital and Knowledge Transfer
Number of intra-organizational ties. Measures of the structural dimension of social capital focus on
the number and pattern of ties that actors have with other actors, irrespective of their relational content
or quality (Wasserman and Faust 1994). Ties among actors provide channels for information exchange
and knowledge flows (Inkpen and Tsang 2005). The more ties an actor entertains, the more opportunities she has to share knowledge and ideas. With an increasing number of intra-organizational ties, it
becomes easier for experts from different parts of the organization to interact, to exchange knowledge,
and to learn from one another (Tsai and Ghoshal 1998). They can inform colleagues about the existence and location of relevant knowledge (Smith et al. 2005). A greater number of direct channels imply faster and increased transfer of different organizational units’ resources, knowledge, and ideas
(McFadyen and Cannella 2004; Burt 1992).
The transfer of project/process management knowledge will predominantly be affected by knowledge
sharing opportunities, i.e. the number of channels through which the knowledge can be distributed,
irrespective of the quality of such channels. This is because project/process management knowledge
can be transferred with comparatively little effort, as we discussed above. Furthermore, the transfer of
project/process management knowledge will also be associated with a larger number of intraorganizational ties because these ties ease the necessary coordination for the application of project/process management knowledge. As we noted above, the coordination of new project or process
rules in an organization will need to be coordinated among a larger number of different organization
members and units. In sum, with an increasing number of intra-organizational ties organization members have more opportunities to transfer and to coordinate their knowledge and ideas across intraorganizational boundaries. We therefore propose:
Hypothesis 1: The number of intra-organizational ties that project team members have with their
colleagues within the organization will be positively associated with the extent of effective project/process management knowledge transfer.
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The Relational Dimension of Social Capital and Knowledge Transfer
The relational dimension of social capital relates to the quality of ties between actors. Tie strength and
trust are attributes of actors’ relational social capital that have figured prominently in research on organizational learning (McEvily and Marcus 2005).
Tie strength. According to Granovetter (1973), strong ties involve frequent interaction, emotional
closeness, and reciprocal services. It has been shown that strong ties facilitate knowledge transfer and
learning (Dyer and Nobeoka 2000; Kale et al. 2000).
Strong ties between exchange partners are particularly beneficial in the case of highly complex, private and difficult to assess knowledge. Due to a strong emotional attachment and expectations of reciprocity, the knowledge sender is willing to invest time and effort in the exchange and to provide help
and assistance even in the case of unplanned requests (Hansen et al. 2001). Moreover, the sender is
motivated to share private resources, such as sensitive knowledge and information (Uzzi and Lancaster
2003; Uzzi 1999). Frequent interactions enhance the recipient’s ability to recognize, to evaluate and to
understand the knowledge transferred. This is because a two-way interaction provides feedback loops
that facilitate learning (Leonard-Barton and Sinha 1993). Strong ties thus facilitate the effective transfer of complex and difficult to assess knowledge. Finally, a high level of social interaction promotes a
long-term perspective between the exchange partners (Larson 1992) and helps to develop cooperative
routines (Uzzi 1997) which further ameliorate knowledge transfer.
Hypothesis 2a: The level of tie strength between project team members and their colleagues within the organization will be positively associated with the extent of effective product/market
knowledge transfer.
Trust. Trust has been defined as positive expectations regarding the goodwill and competence of an
exchange partner (Nooteboom 2002; Das and Teng 2001). Several studies have demonstrated that trust
facilitates the transfer of knowledge between two parties (Muthusamy and White 2005; Uzzi 1996).
In a relationship based on trust, the sender is more willing to share private or sensitive knowledge
because she trusts the recipient to handle the knowledge carefully and to use it in an adequate form
that will benefit the organization (McEvily et al. 2003; Krackhardt 1990). Trust also increases the
openness of a sender in sharing knowledge with a recipient (Nahapiet and Ghoshal 1998). Moreover,
in a trusting relationship, the sender believes that the recipient will not exploit the knowledge for his
own profit or use it against the sender (Das and Teng 2001; Nooteboom 1996). These implications
seem to be particularly salient for transferring complex and highly sensitive knowledge.
From the standpoint of the recipient, trust enhances the motivation to absorb knowledge from a trusted
source (Levin and Cross 2004; Mayer et al. 1995). In a trustful relationship, the recipient assumes a
high level of both reliability and quality of the knowledge that is transferred (Fischer et al. 2002). In
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this way, trust also reduces the recipient’s costs for searching and verifying external knowledge
(McEvily et al. 2003; Gulati 1995). Moreover, a recipient trusting in the good intentions of a sender is
more likely to ask for information, advice or help. This increases the recipient’s opportunities to receive knowledge, even if it is difficult to transfer (Levin and Cross 2004). In a trustful relationship, the
recipient is less concerned about a potential loss of reputation when asking for advice, and as a consequence, trust will positively affect the amount of knowledge transferred (Burt and Knez 1996). Again,
one can argue that these implications of trust for knowledge transfer are particularly important for the
transfer of complex and highly sensitive knowledge. We thus propose:
Hypothesis 2b: The level of trust between project team members and their colleagues within the
organization will be positively associated with the extent of effective product/market knowledge
transfer.
Organizational Performance Effects of Knowledge Transfer
Organizational performance is a multi-dimensional construct (Meyer 2002). Accordingly, scholars
studying organizational performance in general, and the organizational performance implications of
social capital and learning in particular, have applied a number of different performance indicators. As
this study aims at discerning the performance implications associated with the transfer of two kinds of
knowledge contents, project/process management knowledge and product/market knowledge, it seems
appropriate to conceptualize the possible exploitation of this knowledge for improvements in organizational performance in the same way. Accordingly, we shall examine on the one hand organizational
performance indicators that relate to the performance of internal project and process management and
on the other hand indicators for an organization’s product/market performance, specifically the generation of new products, improved sales and access to new suppliers.
Recent work on organizational innovation and performance supports the connection between
knowledge sharing across organizational subunits and the development of new products (Hansen et al.
2001; Hargadon and Sutton 1997), services (Darr et al. 1995) and competitive advantage (Argote and
Ingram 2000). The locus of problem solving and organizational solutions are often dispersed across an
organization’s subunits (von Hippel 1994). Therefore, knowledge and ideas from one subunit can help
to solve the problems of another if the subunits initiate, and engage in, interactions and combine their
knowledge. It has been demonstrated that an organization’s external ties are an important source for
knowledge about novel market trends and customer needs (Ingram 2005; Mowery et al. 1996).
Knowledge sharing with lead suppliers (Dyer and Nobeoka 2000), lead customers (Argote 1999), and
in regional associations (McEvily and Zaheer 1999) have been shown to enhance an organization’s
knowledge base, capabilities, and performance. The subsequent transfer of such knowledge into and
within the organization stimulates learning (Hansen 2002; Argote et al. 2000; Almeida and Kogut
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1999; Zander and Kogut 1995). When the organization acts on and exploits its improved knowledge
base, it will increase innovation and access to new markets. We therefore propose:
Hypothesis 3a: The effective transfer of product/market knowledge from an inter-organizational
project to the organization will positively affect product/market performance.
Inter-organizational projects are also an important source for discovering new forms of managing projects and organizational processes (Ingram 2005; Westney 2002). The transfer of best practices may
serve as a learning device and stimulate the implementation of new management tools and practices in
an organization. The successful transfer and implementation of project and process management
knowledge enhances an organization’s capabilities (McEvily and Marcus 2005). The transfer of such
knowledge thus is an important precondition for its subsequent exploitation and resulting improvements in related organizational performance indicators (Choo and Bontis 2002; Huber 1991). We thus
propose:
Hypothesis 3b: The effective transfer of project and process management knowledge from an inter-organizational project to the organization will positively affect project/process management
performance.
The Mediating Effect of Knowledge Transfer
The hypotheses outlined above propose that social capital affects effective knowledge transfer which
in turn leads to higher levels organizational performance. This mediating role of effective knowledge
transfer, however, is less obvious than it may seem at first sight.
As we outlined earlier, there is some research that has identified direct effects of social capital on organizational performance. This research, however, has largely not scrutinized empirically the processes in which social capital affects organizational performance. While knowledge transfer is one possible process that can help to explain why and how social capital may impact organizational performance, it is not the only one. Alternative explanations seem possible. For instance, McEvily and Marcus (2005) have shown that joint problem-solving mediates between trust and the acquisition of competitive capabilities. Or one could argue on the basis of social comparison theory (Suls et al. 2002) that
social capital affects organizational performance because organization members’ intra-organizational
ties represent a basis for social comparison processes that motivate actors to perform in line with, or
better than, their peers. As alternative explanations are possible, there is some merit in testing explicitly whether knowledge transfer mediates between social capital and organizational performance.
We have suggested above that social capital provides the basis on which an organization learns from
its projects and that only this learning allows an organization to turn its new knowledge into new market opportunities, advanced products, and improved organizational systems and processes. We thus
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submit that effective knowledge transfer is a necessary element in the chain of processes linking features of organization members’ intra-organizational social capital and organizational performance.
Hypothesis 4: Effective knowledge transfer will mediate the relationship between social capital
and organizational performance.
METHODS
Sample
We tested the hypotheses using data from 218 projects directed by 144 firms in the German engineering industry. Most work in the engineering industry is structured and managed in the form of projects
(Hobday 1998; Fong and Lung 2007). Therefore, this context seems particularly suited for studying
how organizations can meet the challenges of cross-project, intra-organizational knowledge transfer
and for examining related performance outcomes.
The engineering industry is heterogeneous in terms of industry sectors, project tasks, project volumes,
and company sizes. Our sample covers all four major sectors of the engineering industry as established
on the basis of the international standard NACE code. Projects represent the full range of tasks in the
engineering industry: the design and production across all types of machinery and equipment, from
single components to highly complex equipment such as production lines and entire factories. Project
volume averaged 42 million €, ranging from 10,000 € to 1 billion €. For inclusion in our sample, projects had to meet the following criteria: they had to be completed within the three years prior to the
study; they had to be inter-organizational projects; and the surveyed company had to be the leading
firm in the inter-organizational project, e.g. as a general contractor. We included companies of different size in our sample. On average, the companies had a turnover of about 3 billion Euros, ranging
from 250,000 to 240 billion Euro, and employed 3,700 employees on average, ranging from a oneperson businesses to major enterprises with 380,000 employees.
The study was conducted via a questionnaire that had been thoroughly pre-tested and revised on the
basis of 20 interviews with industry experts. The experts helped to adjust the design of the survey and
to ensure that no problems existed in terminology or interpretability of questions. They also recommended and validated a key informant approach. They commonly confirmed that in the project-based
engineering industry, project managers are highly involved in and responsible for designing and managing the projects. Therefore, project managers gain deep insights in the project details as well as the
project team’s outside relationships. According to the experts’ assessment and to other studies that
relied on the most knowledgeable key informants (e.g. Lui and Ngo 2004), we therefore relied on the
project managers of the general contractor to answer our questionnaire. During the time period from
January to July 2006, we approached 568 companies. We received questionnaires from 218 project
leaders in 144 firms and thus achieved a response rate of 25.35 %. We checked for possible response
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biases by comparing responding and non-responding firms in terms of company size, sector, and number of employees. We found no significant differences.
Measures
For all independent and dependent constructs, we employed measures based on previously validated
measures wherever possible. With the exception of some control variables, items were measured in a
statement-style on a scale from 1 = fully disagree to 5 = fully agree. Appendix 1 presents details of the
items as well as their sources.
Social capital. As an indicator of the structural dimension of social capital, we applied the number of
intra-organizational ties and measured them by two items: the share of colleagues with whom the
project team members are in contact within the whole organization and within the organization units
relevant for the project. We used tie strength and trust as two indicators for the relational dimension of
social capital. As is common in the literature, we measured tie strength by the two indicators of closeness and communication frequency (Hansen et al. 2001; Reagans and McEvily 2003; Smith et al.
2005). However, following Granovetter’s (1973) original conceptualization of the construct, we also
incorporated a measure of reciprocity. Accordingly, we measured tie strength by three items: the
closeness of relations between project-members and their intra-organizational colleagues, their communication frequency, and the degree to which relations were governed by the norm of voluntary reciprocal assistance. Our measure of trust acknowledges the two main dimensions of trust, actors’ perceived competence and goodwill (Nooteboom 2002). We measured trust on the basis of the following
three items: the degree to which project members and their intra-organizational colleagues could always trust each other to decide and act professionally and competently, to receive all necessary and
reliable information, and to keep their promises.
Effective knowledge transfer. Since this study aims at analyzing organizational performance outcomes of knowledge transfer, we apply a more extensive notion of knowledge transfer than is typical
in the literature. Most studies on knowledge transfer measure whether two actors have shared some
knowledge. While this is a necessary condition for explaining learning-induced changes in actors’
behaviors and organizational performance, it is not sufficient. This is because actors may already have
known what was conveyed to them, and even if the knowledge received is new, they need not act on
their changed knowledge base. A more complete view of the process chain linking knowledge and
changed performance therefore needs to comprise additional sub-processes to the actual sharing of
knowledge. Accordingly, we extend earlier research by employing a notion (and measure) of
knowledge transfer that includes three sub-processes: that knowledge is shared between actors, that
this sharing increases the knowledge of the receiving actor, and that this increased knowledge brings
about actions. When knowledge transfer meets these three conditions, we speak of effective
knowledge transfer.
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Accordingly, we measured effective intra-organizational knowledge transfer by three kinds of items:
the degree to which particular knowledge contents were discussed (two items) among project and other organization members outside the project, the degree to which this knowledge contents increased
the recipients’ knowledge (one item), and the degree to which this increased knowledge then led to
actions (one item). For each of these items, we distinguished two kinds of knowledge contents that can
be transferred. The transfer of product/market knowledge referred to knowledge about the relevant
market on the one hand, and technologies and products, on the other. With respect to the transfer of
project/process management knowledge, items concerned knowledge about organizational processes
and the design and management of projects.
Organizational performance. In line with our measures of knowledge contents, we distinguish organizational performance outcomes that are related to the external market and those that concern intraorganizational project and process management. We measured product/market performance by four
items: access to new markets, contacts with new suppliers, new equipment or production technologies,
and the degree of product and technological improvements. Three items measured project/process
management performance: improvements in project management skills, in project management tools
(such as handbooks and procedures), and improvements in goal attainment in subsequent projects in
terms of schedule, budget, and quality.
Control variables. We added the following industry, firm and project level variables to control for
potential other influences on the dependent variables of our model.
We included the industry sector as a control because sectors in the engineering industry are very heterogeneous in terms of products, customers, suppliers, and more. Based on NACE industry codes, we
included dummies for four sectors of the engineering industry that cover all 29 NACE sub-sectors: (1)
manufacture of machinery for the production and use of mechanical power, (2) manufacture of other
general purpose machinery, (3) manufacture of other special purpose machinery, and, (4) others, including affiliated industries.
Firm size may affect the knowledge transfer and exploitation of an organization, as larger firms have
at hand more resources which may enhance firm innovation and performance. We assessed firm size
by the turnover in the 2005 business year and controlled for it with its natural logarithm. We further
controlled for firm age as it may influence the ability of knowledge transfer and knowledge exploitation (Dodgson 1993). Firm age was measured by the number of years from the founding date until
2006. When the surveyed project belonged to a separate legal entity of the organization, we used the
founding year of this legal entity as basis for firm age. We also controlled for R&D spending as it
might indicate the commitment of a firm to developing their knowledge base (Lane and Lubatkin
1998) and may therefore influence knowledge transfer and performance. R&D spending was measured
in percent of firm turnover in 2005.
15
At the project level, we controlled for project size for the same reasons that we controlled for firm
size. Project size was measured by the project budget and its natural logarithm was entered into the
model as a control variable. Project success may result in more effective knowledge transfer and performance, as people outside of the project are more willing and likely to learn about successful projects than about unsuccessful ones (Smith-Doerr et al. 2004). We measured project success by a dummy variable indicating whether or not the project was successful in terms of having met its schedule,
budget, and quality goals. Project-based organizations can pursue projects that aim at innovation and
learning outcomes and projects that deliver a product or service. Since projects that directly aim at
learning and are set up to produce innovative outcomes are more likely to entail knowledge transfer
and learning outcomes than projects that are intended but to produce a particular product or service
(Child 2001), we also controlled for learning intent. We measured learning intent in form of a dummy
variable that indicates whether or not learning from the project was part of the rationale for the project.
Validity and Reliability of Measures
To test for convergent validity of the multi-item constructs, we conducted principal component factor
analyses for all independent and dependent variables. Standardized factor loadings for all items are
above the recommended minimum of 0.40 (Ford et al. 1986), the majority are even close to or above
0.80. Average variances extracted by the factors are all above the recommended minimum of 0.50
(Fornell and Larcker 1981). Furthermore, Cronbach’s α is above the recommended minimum of 0.70
for all factors besides tie strength (see Appendix 1). Cronbach’s α for tie strength remains slightly
below the recommended 0.70 level but it still seems acceptable due to the established theoretical foundation for the construct. Positive correlations among the three social capital constructs indicate convergent validity as these constructs characterize different dimensions of the same concept. We crossvalidated the figures for three control variables—turnover, firm age, and R&D spending—via published records on the company websites and/or with senior project managers for a sub-sample of 60
firms that took part in the survey. We found that these figures reliably matched the figures reported in
the individual surveys. All in all, the constructs in this study show good internal consistency and reliability.
We tested the discriminant validity of our constructs by determining whether the variance extracted
from each construct is higher than the squared correlation between the constructs (Fornell and Larcker
1981). This test revealed that all of our constructs demonstrate discriminant validity.
Finally, we controlled for common method variance with Harman’s one-factor test (Podsakoff and
Organ 1986). An exploratory principal component factor analysis of all items in the hypothesized
model resulted in 11 factors explaining 69 % of the variance; dependent and independent variables
loaded on different factors. Therefore, common method variance is unlikely to be a problem. Table 1
below reports means, standard deviations, and correlations for all constructs in the study.
16
Model Specification and Analysis
We tested our hypotheses by means of structural equation modeling using maximum likelihood estimation. We extracted factors for our constructs in principal component factor analyses. On this basis,
we calculated additive indices from the respective items of these constructs and included them in a
path model. In addition to the paths specified in Figure 1 below, we included all control variables as
exogenous factors that could affect the mediating and dependent variables in the model. We then omitted from the model those variables that had no significant effect on the mediating and dependent variables.
In addition to the relations spelled out in our hypotheses, we also specified paths between some variables. According to Tsai and Ghoshal (1998), the different dimensions of social capital may influence
one another. We therefore included in our model covariances between the three variables measuring
social capital. We also introduced in the model relations between the two variables measuring
knowledge transfer as well as between of our two organizational performance measures. This is because the transfer of product/market knowledge may take place in conjunction with the transfer of
project and process management knowledge. For instance, actors may communicate about how a new
product idea or market penetration could be realized organizationally or which beneficial or detrimental effects a change in project and/or process management may have for the firm’s market position
or product offering. Likewise, improvements in project and process management will have implications for product and market performance; conversely, when organizations access new markets or new
suppliers this will often require changes in their process management.
In a first analytical step, we tested hypotheses 1, 2 and 3 on the basis of the structural equation model
represented in figure 1 below (for visual clarity, this figure does not show the control variables). In a
second step, we both established the validity of our resulting model and examined hypothesis 4 concerning the mediating role of knowledge transfer on the basis of nested model tests (Loehlin 1992).
RESULTS
Figure 1 shows the path coefficients, covariances and their respective significance for the specified
structural equation model. Table 1 presents the means, standard deviations, and correlations for all
constructs in the model. Table 2 shows parameter estimates for the control variables.
Insert Figure 1 about here
Insert Table 1 about here
Insert Table 2 about here
Hypotheses 1 predicted that a larger number of intra-organizational ties with other organization members will facilitate the effective transfer of project/process management knowledge. This hypothesis is
fully supported. Hypothesis 2 suggested that strong ties (H2a) and trust (H2b) between project mem-
17
bers and their colleagues in the organization augment the effective transfer of product/market
knowledge. We find support for the positive relation between tie strength and the effective transfer of
product/market knowledge. However, contrary to our expectations trust does not significantly affect
effective transfer of product/market knowledge. The significant strong positive covariance between tie
strength and trust, however, suggest that trust might indirectly affect the effective transfer of product/market knowledge. We therefore explored revised models that allowed for indirect relationships
between the social capital constructs and effective knowledge transfer. Results show a significant direct effect of trust on tie strength, with a strong impact of tie strength on the effective transfer of product/market knowledge, thus indicating an indirect impact of trust on the effective transfer of product/market knowledge.
Hypothesis 3 concerned the relation between effective knowledge transfer and organizational performance. We studied relations between increased project/process management knowledge and improvements in project and process management performance (H3a) and increased product/market
knowledge and improved product and market performance (H3b). Both postulated relations are strongly and significantly supported by the data.
None of the studied control variables have a significant impact on our measures of organizational
performance outcomes. Firm size and industry sector show no significant effects on either kind of
effective knowledge transfer. The effective transfer of product/market knowledge is furthered, if the
projects in which the knowledge transferred originated had the intent of producing learning effects and
were successful. With increasing age, however, organizations seem to be less effective at the transfer
of product/market knowledge. The effective transfer of project/process management knowledge is
augmented in case of larger project budgets. Higher levels of firms’ R&D spending as well as the success of a project, however, have negative implications for the effective transfer of project/process
management knowledge.
We examined various goodness-of-fit statistics to assess overall model fit. In all, the fit indices for the
tested model as presented in Table 3 below and the overall model fit can be considered to be good.
The AGFI (adjusted goodness of fit index) is a normed version of the GFI which compares predicted
squared residuals with obtained residuals and values above 0.90 are desirable (Jöreskog and Sörbom
1984). The RMSEA (root-mean-square error of approximation) indicates better fit the closer it is to
zero; a RMSEA below 0.10 is regarded as indicating acceptable fit (Browne and Cudeck 1993). For
Bentler’s CFI (comparative fit index) values above 0.90 are desirable (Bentler 1990). The BentlerBonett NFI (normed fit index) indicates the extent to which the model improves fit compared to a random model and the given NFI value indicates that the model could be improved still but is acceptable
concerning model fit (Bentler and Bonett 1980). The normed chi-square statistic (Chi²/d.f.) should be
below 2.0 to indicate a good fit (Hair et al. 1995).
18
To further assess the fit of the model, we employed nested model tests. These tests compare the chisquares of models with a different number of paths with one another in order to validate a prespecified model. More complex models are to be preferred for the data if they result in a significant
difference in chi-square (Loehlin 1992).
Insert Table 3 about here
Table 3 above presents results for the four models we compared using sequential chi-square difference
tests: (1) a null model with no relationships between the constructs, (2) a saturated model that includes
both direct and indirect relationships between the independent social capital constructs and the organizational performance outcomes, (3) the pre-specified model with only indirect effects of social capital
constructs on organizational performance mediated by effective knowledge transfer, (4) a direct model
that only allows direct effects between the social capital constructs and organizational performance
outcomes.
The testing sequence of these models is presented in Table 4 below. First, both goodness-of-fit indices
(Table 3) and the chi-square difference test show that the saturated model (model 2) represents a better
fitting model than the null model (model 1). Second, the specified and the saturated model are compared. The specified model lacks the direct paths from the number of intra-organizational ties and tie
strength to organizational performance. Goodness-of-fit statistics are almost identical for both models
and none of the direct paths in the saturated model are significant. The chi-square difference test
proves insignificant for these two models and therefore indicates that the more prudent model provides
a better fit with the data: the specified model (model 3). The direct model (model 4) shows goodnessof-fit statistics inferior to the specified model (model 3). A chi-square difference test of these two
models is not possible as they are not nested. A comparison of the saturated model (model 2) with the
direct model (model 4) shows a significant difference in chi-square which indicates preference for the
saturated model. We conclude that the results of the nested model tests show that the specified model
(model 3) fits the data best and, thus, is to be preferred over the other models.
Insert Table 4 about here
To test the mediating role of effective knowledge transfer in regard to the relationship between social
capital and organizational performance as spelled out in hypothesis 4, we compared the path coefficients of the specified, the saturated, and the direct models. Results of the nested model tests indicate a
better fit for the specified mediation model with the data than for the direct effects model (model 4)
and the saturated model (model 2), thus indicating support for hypothesis 4.
To further test the mediation thesis, we also examined the three conditions for mediation noted by
Baron and Kenny (1986): First, the independent variables must affect the mediator; second, the mediator must affect the dependent variable; and third, when including the mediating effect, previously sig-
19
nificant relationships between independent and dependent variables should be eliminated or substantially reduced. Results for the test of these three conditions are presented in Table 5 below. They show
that all three conditions are satisfied thus confirming the mediating role of effective knowledge transfer in the relationships between dimensions of social capital and organizational performance outcomes.
Insert Table 5 about here
DISCUSSION AND CONCLUSION
Our study examined whether and how project members’ social capital contributes to overcoming the
formidable barriers to effective knowledge transfer that exist in project-based organizations and thus
enhances organizational performance. Results show that project members’ social capital facilitates
effective intra-organizational knowledge transfer across project boundaries that in turn improves organizational performance. In extension of earlier studies, our hypotheses and findings however suggest
that project members’ social capital does not have uniform implications across different kinds of
knowledge contents and related performance outcomes. Specifically, we proposed and find that the
effective transfer of knowledge relating to project and process management is positively affected by
our indicator of the structural dimension of social capital, the number of project members’ intraorganizational ties. While the effective transfer of product- and market-related knowledge depends on
the strength of project members’ intra-organizational ties and indirectly on trust, both indicators of the
relational dimension of social capital. In addition, we find that a number of control variables that we
included in our study also discriminatingly affect the two kinds of knowledge contents. According to
our results, the effective transfer of project and process management knowledge is augmented by larger project budgets but reduced by project success as well as higher levels of R&D spending in the
firm. The effective transfer of product/market knowledge, on the other hand, is boosted when projects
have been successful and aimed at learning effects but declines with firm age.
These findings have several implications for theory, specifically for the literatures on project-based
organizations, social capital, and knowledge management. Our study contributes to the literature on
project-based organizations by improving our limited understanding of key drivers of cross-project
learning and organizational performance (Bresnen et al. 2005). While we know that knowledge sharing among different organizational subunits is a source of competitive advantage (Argote and Ingram
2000; Gupta and Govindarajan 2000), research is well aware of the specific difficulties faced by project-based organizations in passing knowledge and ideas from one project to the next (DeFillippi and
Arthur1998). Our study indicates that social capital helps to bridge the critical intra-firm boundaries
that arise due to the dispersed and discontinuous nature of project-based work by enhancing organization members’ opportunity, motivation and ability to share knowledge across projects. Moreover, we
find that project-based organizations that internally transfer knowledge effectively get access to novel
20
markets, improve their products and technologies and perform better in subsequent projects. We therefore conclude that intra-organizational social capital between project team members and their colleagues outside the project is an important driver of the effective transfer of knowledge, which in turn
enhances organizational performance.
Second, our study contributes to an emerging stream of literature examining the relations between
social capital, intra-firm knowledge transfer, and organizational performance (Yli-Renko et al. 2001;
Tsai and Goshal 1998). Specifically, our results in the particularly challenging context of projectbased organizations demonstrates that effective knowledge transfer presents an important mediating
process by which social capital affects organizational performance. In extension of earlier studies, we
however find that social capital does not have uniform effects on knowledge transfer and subsequent
organizational performance. Rather, different dimensions of social capital discriminatingly affect different kinds of knowledge contents and their related performance outcomes. The structural dimension
of social capital we studied seems to enhance project members’ opportunities for knowledge sharing.
With increasing numbers of internal contacts, project members can more easily and widely distribute
their knowledge. This effect, however, according to our results does not hold for all kinds of
knowledge, but mainly for project/process management knowledge that is relatively easy to transfer.
In contrast, the relational dimensions of social capital we studied seem to affect transfer partners’ motivation and ability to engage in the transfer of project/market knowledge, which is comparatively
more difficult and demanding. These findings support research promoting a more differentiated and
contingent view of social capital (Burt 1997), as they suggest that some kinds of social capital might
be beneficial for some purposes but ineffective or even detrimental for others (Maurer and Ebers 2006;
Gargiulo and Benassi 2000). However, at the same time these findings also cast doubt on the theoretical fruitfulness of using the concept of social capital as a theoretical umbrella concept. If the different
constituent elements of social capital show divergent implications, there seems to be little added benefit when one subsumes these different dimensions under one theoretical umbrella term.
Third, our study adds to the literature on knowledge management and learning by highlighting that
knowledge contents discriminatingly impacts effective knowledge transfer. Prior studies already
demonstrated that the relationship between social capital and effective knowledge transfer is contingent on knowledge related attributes such as its complexity (Hansen 1999), causal ambiguity (Szulanski et al. 2004), or the implicit nature of knowledge (Levin and Cross 2004). Our findings suggest that
knowledge content, i.e. whether knowledge concerns the firm’s external market and its products or the
firm’s internal project and process management, represents an additional discriminating dimension of
knowledge that is relevant for the ease of knowledge transfer and its consequences. The fact that some
of our control variables are also significantly related to the different kinds of knowledge contents further underscores the validity of the conceptual differentiation between product/market and project/process management knowledge.
21
There are several theoretical and empirical limitations to this study that might indicate fruitful opportunities for future research. Because our empirical study rests on a single industry, further studies in
other industries are needed to test the generalizability of our argument. More specifically, the kinds
and roles of knowledge contents in the technology driven engineering industry might differ from the
relevant knowledge contents in other project based industries such as advertising or film making.
Moreover, while we established discriminating effects associated with different kinds of knowledge
contents, we need to know more about the underlying dimensions of knowledge contents that can help
to explain the discriminating effects. In contrast to much of the literature on knowledge management
and learning, we took the perspective of the knowledge sender and her social capital, rather than that
of the recipient. A more complete picture, of course, would require data on both parties in an exchange
relation. A further opportunity for future research could be to examine in more detail the three subprocesses of effective knowledge transfer that we distinguish, and their respective antecedents and
consequences. It may well be that different influence factors affect whether knowledge sender and
recipient discuss particular knowledge contents, whether this increases the parties’ knowledge base,
and whether the parties then use their new knowledge and act upon it. In addition, future research
could explore how different sources and ways of sourcing particular knowledge contents may impact
knowledge transfer processes and their contingencies.
Despite these open questions, this study contributed to illuminating how project-based organizations
can bridge the barriers of effective knowledge transfer and thus improve learning and organizational
performance. In conclusion, our study points out that in addition to established formal and technological means, such as knowledge management procedures and information systems, organization members’ intra-organizational social capital plays an important role in facilitating effective intraorganizational learning and organizational performance in project-based organizations.
22
Figure 1. Specified model with standardized maximum likelihood parameter estimates for path coefficients and covariances.
Number of Intraorganizational Ties
0.163**
Project/Process
Management
Knowledge
0.381***
0.194**
Effective Knowledge Transfer
Tie Strength
0.226***
0.550***
0.324***
Product/Market
Knowledge
0.390***
Organizational Performance
Project/Process
Management
Performance
0.344***
0.648***
Product/Market
Performance
n.s.
Trust
This simplified version of the actual model omits error terms and control variables. An exogenous unobserved
error variable was attached to each of the endogenous variables (constructs for effective knowledge transfer and
organizational performance) to account for the variance not explained by the observed exogenous variables. The
error coefficients were fixed to unity to enable model identification. All variables in this simplified version of the
results model are latent variables, represented by ovals.
***
p≤0.001; **p≤0.01
23
Table 1. Means, standard deviations, and correlations for all constructs in the model
1.
2.
3.
4.
5.
6.
7.
8.
9.
Mean
S.D.
3.53
1.08
2.
Number of intraorganizational ties
Social interaction
3.32
0.79
.381 ***
3.
Trust
3.96
0.60
.194 **
.390 ***
4.
2.91
0.93
.171 *
.078
2,87
0,87
.154 *
.258 ***
2,63
0.95
.116 +
.102
2.48
0.88
.099
.249 ***
.064
8.
Effective knowledge transfer –
project/process management
knowledge
Effective knowledge transfer –
product/market knowledge
Organizational performance –
project/process management
performance
Organizational performance –
product/market performance
Firm size
18.96
2.57
-.393 ***
-.247 ***
.033
-.015
-.025
-.029
-.017
9.
Firm age
67.88
57.36
-.133 *
-.039
.090
-.065
-.135 *
-.101
-.056
.254 ***
15.15
2.45
-.123 +
-.095
-.098
-.060
.063
-.037
.509 ***
11. Industry: mechanical
power machinery
12. Industry: general
purpose machinery
13. Industry: special
purpose machinery
14. Industry: others and
affiliated industries
15. R&D spending
0.11
0.32
.038
.006
-.032
-.044
.113 +
16. Project success
17. Learning intent
1.
5.
6.
7.
10. Project size
0.26
0.44
.142
0.33
0.47
0.30
*
10.
11.
12.
13.
14.
15.
16.
-.032
.043
.303 ***
-.031
.609 ***
.338 ***
.171 *
.651 ***
.114 +
.141 *
.040
.042 +
.442 ***
.063
.019
.005
-.090
.052
.004
-.197
-.003
.090
.077
.020
.016
-.052
.015
.014
0.46
-.159 *
-.155 *
-.176 **
-.053
.040
.026
.012
.095
5.92
8.40
.081
.027
.008
-.124 +
.102
-.061
.146 *
0.89
0.31
.091
.083
.100
-.059
.195 **
.029
.131 +
0.15
0.36
.056
.089
.066
.059
.190 **
.103
.117 +
**
.184 **
-.024
-.064
-.019
-.042
.123 +
-.212 **
.210 **
-.250 ***
-.409 ***
-.091
-.130 +
-.237 ***
-.387 ***
-.458 ***
-.048
-.061
-.152 *
-.001
-.075
-.043
.098
.030
.048
-.017
-.071
.048
.031
.052
-.118 +
-.056
-.150 *
-.027
.023
.016
-.019
-.014
.116 +
.016
N=218, for firm size and firm age, natural logarithms are used in correlations
***
p≤0.001; **p≤0.01; *p≤0.05; +p≤0.10 (two-way significances)
24
Table 2. Structural equation modeling results for the control variables (standardized maximum likelihood path coefficients)
Path
coefficient
Description of path
Firm size → Effective Effective knowledge transfer (project/process management knowledge)
Firm size → Effective Effective knowledge transfer (product/market knowledge)
Firm size → Organizational performance (project/process management knowledge)
Firm size → Organizational performance (product/market knowledge)
Firm age → Effective Effective knowledge transfer (project/process management knowledge)
Firm age → Effective Effective knowledge transfer (product/market knowledge)
Firm age → Organizational performance (project/process management knowledge)
Firm age → Organizational performance (product/market knowledge)
Project size → Effective knowledge transfer (project/process management knowledge)
Project size → Effective knowledge transfer (product/market knowledge)
Project size → Organizational performance (project/process management knowledge)
Project size → Organizational performance (product/market knowledge)
Industry → Effective knowledge transfer (project/process management knowledge) - tested for all four sub-industries
Industry → Effective knowledge transfer (product/market knowledge) - tested for all four sub-industries
Industry → Organizational performance (project/process management knowledge) - tested for all four sub-industries
Industry → Organizational performance (product/market knowledge) - tested for all four sub-industries
R&D spending → Effective knowledge transfer (project/process management knowledge)
R&D spending → Effective knowledge transfer (product/market knowledge)
R&D spending → Organizational performance (project/process management knowledge)
R&D spending → Organizational performance (product/market knowledge)
Project success → Effective knowledge transfer (project/process management knowledge)
Project success → Effective knowledge transfer (product/market knowledge)
Project success → Organizational performance (project/process management knowledge)
Project success → Organizational performance (product/market knowledge)
Learning intent → Effective knowledge transfer (project/process management knowledge)
Learning intent → Effective knowledge transfer (product/market knowledge)
Learning intent → Organizational performance (project/process management knowledge)
Learning intent → Organizational performance (product/market knowledge)
n.s.
n.s.
n.s.
n.s.
n.s.
-0.124
n.s.
n.s.
0.166
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
-0.137
n.s.
n.s.
n.s.
-0.138
0.179
n.s.
n.s.
n.s.
0.162
n.s.
n.s.
n.s.
+
**
*
*
**
*
p≤0.001; **p≤0.01; *p≤0.05; +p≤0.10
n.s.: not significant
***
Table 3. Model statistics
Model
1.
2.
3.
4.
Null model
Saturated model
Specified (mediation) model
Direct effects model
Chi²
d.f.
AGFI
RMSEA
CFI
NFI
Normed
chi²
464.746
51.572
54.338
257.036
66
49
51
51
0.680
0.938
0.938
0.781
0.167
0.016
0.017
0.136
0.000
0.994
0.992
0.483
0.000
0.889
0.883
0.447
7.042
1.052
1.065
5.040
Table 4. Nested model testing sequence and difference test
Comparison
Model 2 vs. 1
Model 3 vs. 2
Model 4 vs. 2
Saturated vs. null
Specified vs. saturated
Direct vs. saturated
Chi² diff.
d.f. diff
p
Model
preference
413.173
2.766
205.464
17
2
2
<0.001
>0.10
<0.001
2
3
2
25
Table 5. Test of mediation: comparison of standardized path coefficients for the direct, mediation,
and saturated models
Path
Direct
model
Mediation
model
Saturated
model
Number of intra-organizational ties → Effective knowledge transfer (project/process management knowledge)
Number of intra-organizational ties → Organizational performance (project/process management knowledge)
0.163
0.073
**
0.163
**
0.163
-0.013
**
Tie Strength → Effective knowledge transfer (product/market knowledge)
Tie Strength → Organizational performance (product/market knowledge)
0.226
0.249
***
0.226
***
0.226
0.088
***
0.626
0.552
***
Effective knowledge transfer → Organizational performance (each: product/market knowledge)
Effective knowledge transfer → Organizational performance (each: project/process management knowledge)
***
0.648
0.550
***
***
+
***
The covariances between the social capital constructs, the relations between the different knowledge contents for
both effective knowledge transfer and organizational performance as well as the path coefficients of the control
variables project size, firm age, R&D spending, project success, and learning intent were included in each model.
Since these effects are reported in Figure 1 and Table 2, they are omitted here.
***p≤0.001; **p≤0.01; *p≤0.05; +p≤0.10
Numbers in bold indicate where mediation is possible.
26
Appendix 1. Measurement of constructs
Variable
Items
CONTROL VARIABLES
Source and Scale
Industry sector
This project can be assigned to the following sub-sector:
(a) manufacture of machinery for the production and use
of mechanical power
(b) manufacture of other general purpose machinery
(c) manufacture of other special purpose machinery
(d) others, including affiliated industries.
Dummy variables for each
of the four industry subsectors
Firm size
Please specify the turnover of your organization in the
preceding business year.
Natural logarithm of the
turnover
Firm age
In which year was your organization founded (in a corporate group, please relate to the smallest legal entity,
e.g. SBU)?
Years from founding date
until 2006
R&D spending
What was the percentage of R&D spending in turnover
in the preceding business year?
Project size
The total budget of our project amounted to:
Natural logarithm of the
total budget
Project success
Was the project successful in terms of having met its
schedule, budget, and quality goals?
Dummy variable
Learning intent
Please indicate whether you conducted this project in
order for your organization to learn from it.
Dummy variable indicating
whether or not the project
was conducted to learn
(a) Our project team members (on a professional and/or
private level) were in touch with most of their colleagues in the rest of the company (in a corporate
group, please relate to the smallest legal entity, e.g.
SBU).
(b) Our project team members (on a professional and/or
private level) were in touch with most of their colleagues in the departments relevant for this project.
5-point Likert scale ranging
from (1) fully disagree – (5)
fully agree
(a) Our project team members and their colleagues in
our company were very close to each other.
(b) Our project team members and their colleagues in
our company communicated very often with each
other.
(c) Our project team members and their colleagues in
our company abided by the norm that voluntary assistance by someone else in the company would be
reciprocated eventually.
(a)/(b) following Reagans
and McEvily 2003, Hansen
et al. 2001
(a) Our project team members and their colleagues in
our company could always trust that each would decide and act professionally and competently.
(b) Our project team members and their colleagues in
our company could always trust that each would receive necessary and reliable information and service.
(c) Our project team members and their colleagues in
our company could always trust that each would
keep the promises they make.
(b) following Tsai 2000
(c) following Tsai and
Ghoshal 1998
SOCIAL CAPITAL
Number of intraorganizational
ties
Cronbach’s α 0,732
Tie strength
Cronbach’s α 0,623
Trust
Cronbach’s α 0,784
5-point Likert scale ranging
from (1) fully disagree – (5)
fully agree
5-point Likert scale ranging
from (1) fully disagree – (5)
fully agree
27
EFFECTIVE KNOWLEDGE TRANSFER
Introductory sentence: In the following questions, please state how many ideas were forwarded from the project
into your organization.
Product/market
knowledge
Cronbach’s α 0,716
Project/process
management
knowledge
Cronbach’s α 0,820
(a) With our colleagues outside of the project (i.e. in the
company or in other projects), we discussed a lot
about our market.
(b) With our colleagues outside of the project (i.e. in the
company or in other projects), we discussed a lot
about technologies and products.
(c) Through this project, colleagues outside of the project (i.e. in the company or in other projects) obtained a tremendous amount of market knowledge
(i.e. about that market, competitors, and possible customers).
(d) Through this project, many new projects to (further)
develop products and/or technologies have been initiated.
(c)/(d) following Yli-Renko
et al. 2001
(a) With our colleagues outside of the project (i.e. in the
company or in other projects), we discussed a lot
about the successful management and implementation of projects.
(b) With our colleagues outside of the project (i.e. in the
company or in other projects), we discussed a lot
about our internal processes.
(c) Through this project, colleagues outside of the project (i.e. in the company or in other projects) obtained a tremendous amount of knowledge about
possible improvements of our internal processes.
(d) Through this project, many new projects to improve
our internal processes have been initiated.
5-point Likert scale
ranging from (1) fully disagree – (5) fully agree
5-point Likert scale ranging
from (1) fully disagree – (5)
fully agree
ORGANIZATIONAL PERFORMANCE
Introductory sentence: In the following questions, please state how many ideas were implemented in your organization.
Product/market
performance
Cronbach’s α 0,741
Project/process
performance
Cronbach’s α 0,832
(a) As a result of the project, our organization could
access a lot of new markets for our business.
(b) As a result of the project, our organization could
access a lot of new contacts or suppliers.
(c) As a result of the project, our organization could
access a lot of new equipment or production processes to (further) develop our products.
(d) As a result of the project, our organization could
improve our products/technologies tremendously.
(a)/(b)/(c) following Human
and Provan 1997
(a) As a result of the project, our organization could
tremendously improve our project management
skills.
(b) As a result of the project, our organization could
tremendously improve our project management tools
(e.g. handbooks, project plans, project controlling).
(c) As a result of the project, our organization could
tremendously improve achieving our project goals
(in terms of meeting schedule, budget and quality) in
succeeding projects.
(a) following Human and
Provan 1997
5-point Likert scale ranging
from (1) fully disagree – (5)
fully agree
5-point Likert scale ranging
from (1) fully disagree – (5)
fully agree
28
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